Spectroscopy and spectral imaging have significantly progressed over the last hundred years employing the electromagnetic spectrum. However, spectroscopy has been developed for specific energy ranges, for example, x-ray dispersion, ultraviolet, visible, infrared, and microwave spectroscopies. These spectroscopic methods have adopted different units to describe the spectra derived within their specific energy range. For example, KeV is used for x-ray units, nm for ultraviolet and visible, wavenumber for infrared, and G-Hertz for microwave. Since these differing nomenclatures are different ways to refer to the electromagnetic spectrum, they can all be converted to a meter scale ranging from 10−1 to 108 nm (nanometers). With this common unit, it is easy to understand the spectroscopic relationships across the electromagnetic energy range as shown in FIG. 1.
Spectral imaging, represented by multi-spectral and hyper-spectral imaging, is the formation of images constructed from spatial data describing a field of view and spectral data that provides correlated information to the positions of the materials in the field of view. With the advent of multi-spectral and hyper-spectral imaging, the concept of a spectral signature or fingerprint has been used since the spectra obtained from these imaging techniques is associated with a spectral description and definition a particular material. These terms are appropriate due to the complexity of the resulting spectra from a material of interest comprised of a multiple different molecules and compounds. Moreover, the clarity of hyper-spectral images and positive identification of the presence of specific elements, molecules and compounds is impeded by irrelevant spectral components, e.g., the illumination, background and instrument noise. All these spectral components significantly lower the signal to noise ratio. To better understand this phenomenon, FIG. 9 shows simulated spectra illustrating all the spectral components overlaid for ease of understanding. Illumination spectra of an illuminated sample material is illustrated by the area under the curve (A). Once the sample material is illuminated with the corresponding illumination source it will absorb some of that illumination energy as represented by the negative area (C) and will also emit or reflect some of that illumination energy as illustrated by the area under the curve (D). However, as previously explained noise as represented by (B) will always be present as part of the spectral components of a signal.
In order to understand the importance and the novelty of the present invention, it is necessary to define the concepts of intrinsic and irrelevant. The word intrinsic means, “belonging to a thing by its very nature” and is synonymous with terms like inherent, basic, essential, fundamental, central, core, key, innate, and underlying. On the other hand, the word irrelevant is associated with being immaterial, unrelated, extraneous, and unconnected. The basis of intrinsic spectroscopy and intrinsic spectral imaging according to the present invention, is built on these concepts and how they are applied to spectroscopy for producing consistent spectral signatures (fingerprints) independent of factors not associated with the material of interest, whether it is a single elemental, a molecular material, or a highly complex mixture of compounds. Therefore, Intrinsic Spectral Components are spectral component that arise from absorption of the illumination energy and Irrelevant Spectral Components are spectral components of the illumination energy that are not absorbed.
The basic principle of intrinsic spectroscopy is that intrinsic spectral components of a material of interest can only be generated when the material of interest absorbs energy. Once energy absorption occurs then that energy can interact with the material in different ways depending on the wavelength of the absorbed energy and the atomic content and molecular structure of the material of interest through atomic energy transitions and molecular bonding response, respectively. Any energy impinging on the material of interest that is not absorbed does not contribute to the material's intrinsic spectrum and is considered irrelevant.
The full range spectral signature or fingerprint of a material of interest would contain only the spectral components of the material of interest without irrelevant components from the illumination and background associated with spectrometer devices and sample chambers; this can be considered an intrinsic signature or fingerprint that is an assembly of intrinsic spectra across the full wavelength range.
Illumination Component
In general, an electromagnetic spectrum is generated when an atom, molecule, compound or material is illuminated by electromagnetic energy and absorbs specific ranges of the illumination energy, i.e., the absorption envelopes. The type of spectrum generated depends on how the sample (atom, molecule, compound or material) responds to the absorbed energy. It may allow the energy through (transmission spectroscopy), retain the energy (absorption spectroscopy), raise electrons to higher orbitals before dropping back to lower orbitals (fluorescence spectroscopy), or shift the illumination to a fixed wavelength distance (Raman Spectroscopy). In all cases, the absorption envelopes have a specific wavelength range with specific extinction coefficients for each wavelength within the absorption envelope. This describes the spectral profile/shape of the envelope. In classical spectroscopy, filters or lasers of narrow wavelengths are used that fall close to the maximum absorption of the envelope to illuminate sample of interest. However, when illuminating the sample with a wide range of wavelengths, only wavelengths that fall within the envelope are absorbed and can give rise to spectral components of the atom, molecule, compound or material in the sample. The tradeoff between these illumination conditions is resolution verses signal strength, respectively. All illumination wavelengths that are not absorbed can be considered as irrelevant spectral components of the illumination.
Background Component
Spectral components that are derived from background materials, e.g., the surface on which the sample resides or in the solution the sample is dissolved or suspended, may or may not be considered irrelevant in the analysis. This is determined on whether or not the background is important to the analysis and if the background is not included in the reference chamber. For a specific preparation of the sample, the irrelevant illumination and background spectral components are consistent and reproducible with respect to the resulting spectrum.
Environment Component
Environmental factors need to be considered as they affect the spectral response of a material of interest. These are variable conditions that affect the response of the material of interest, e.g., pH or the hydrophobicity of the support medium.
Instrument Noise
The spectral components arising from the instrument are considered random noise and vary each time a spectrum is obtained from the same specific sample, illustrated in FIG. 5 and FIG. 7. Random noise spectral components derive from electronic, mechanical and heat from the particular instrument. These spectral components will be present even when there are no sample or reference materials in the instrument. Since the instrument noise is random, and thus cannot be predicted, it cannot be absolutely removed from the intrinsic spectrum. Fortunately, the level of electronic noise in modern instrumentation is negligible, e.g., <2%, compared to the irrelevant illumination and background components. The method of removing the irrelevant spectral components of illumination has been described by the methodology explained in U.S. Pat. No. 9,435,687 to Schwartz et al, incorporated herein by reference in its entirety.
Spectral Wavelength Range
Another limitation of multi-spectra and hyper-spectra is the electromagnetic wavelength range available from the individual instrument's illumination energy that limit the spectral information available from the instrument. Consider the analogy of a person's name, e.g., John Smith. This information is not sufficient to identify a specific person within the population of the United States. However, if the name was Prof. Dr. John Benjamin Smith, III, this name most likely provides enough information to positively identify the person within the said population. In a similar manner, just having the visible spectrum of a compound indicating that it is red, is not sufficient to identify a molecule, such as, phycoerythrin. The present state of the art of hyper-spectral imaging can only make inference to the presence of phycoerythrin in a hyper-spectral image from the indication of a red spectral component occurring where phycoerythrin fluoresces and prior knowledge of the content of the sample, e.g., red-tide algae were known to be present in the imaging area. Positive identification requires that both infrared and fluorescence spectra show consistent chemical bonding and the wavelength positions and separations of the absorption (inverted excitation) and emission peaks, i.e., the Stoke's shift. The spectral signature and absorption envelope for this example could range from the near ultra violet (400 nm) to the far infrared (2000 nm).
A complete intrinsic spectral signature of a molecule or compound would theoretically encompass the whole electromagnetic range from wavelengths from zero to infinity. However, an achievable practical extended wavelength spectrum may range from 0.1 Angstrom (a electron beam) to 100 centimeter (microwaves). Such a spectrum would contain intrinsic spectral components from elements obtained from x-ray spectroscopy, transition state spectral components from UV, visible and infrared spectroscopy and size and rotational spectral components from microwave spectroscopy. By simply combining spectra from classical spectrometry does not provide an acceptable continuous spectrum when filters and laser lines are used for illumination. The combined spectra would have gaps caused by the absorption gaps in the wavelength range due to incomplete illumination across the absorption wavelength range and the lack of intensity normalization across the different instrumentation.
Thus, what is needed is a system and a method for obtaining a full range intrinsic spectral signature for spectroscopy and spectral imaging without the limitations of the current technologies.